Enable the Team
The architect's job is changing. Every practice can get the same AI tools. The ones that pull ahead have architects who know how to use the tools they have, in the context of their own work, to deliver with speed and at scale. We teach your architects those skills on your own repository and mentor them until the new way of working is how work gets done.
"If you want to improve productivity, focus on skills, not tools. AI amplifies expertise; it doesn't manufacture it. To use the AI capabilities well, you have to know how to do the architecture."Ryan Schmierer, The Value Shift
We do not sell the tools. We teach the people who use them.
The AI options for Sparx EA are available to every practice, and some of them are free. None of them changes a practice on its own. The multiplier comes from architects who can articulate what they want and judge what comes back, and that is a human skill, learned by doing, with someone alongside who has done it before.
The sooner your architects learn it, the sooner the capacity of your practice multiplies. Training and mentoring is how we help, and it is the work we are paid for.
The architect's job is changing
It is not about the tools. When modeling, analysis and governance were done by hand, the capacity of a practice was set by how fast its architects could operate the architecture tool. With AI-Augmented Architecture the mechanics move to AI, and capacity is set by how well an architect can say what they want and how quickly they can review what comes back. That is a different job, and it takes different skills.
- Transcribing documents and spreadsheets into the model, one element at a time
- Drawing and redrawing diagrams for each audience
- Reading model contents by hand in review boards
- Formatting reports and decks for stakeholders who will never open EA
- Capacity set by how fast one person can work the tool
- Stating clearly what should be built, analyzed or checked, and from which sources
- Reviewing what AI produced and deciding whether it is right
- Defining the standard once and letting the repository enforce it
- Sitting with stakeholders on the business problem while the answers come from the model
- Capacity set by how well the architect can articulate and judge
Four skills, one for each place the work changes
The skills are the same whichever AI option your practice chose. They are learned fastest on your own repository, on work your architects have open now.
Modeling: hand over the inputs, get a model, review it
Describing the scope and the sources, from requirements and project specs to the systems that hold your data, so AI builds the model in your modeling language, then reviewing the result against the standard instead of building it by hand.
AI-Augmented Modeling in Sparx EAAnalysis: ask the question of the whole enterprise
Framing a question so AI can answer it from the model together with the documents, spreadsheets, operational systems and external sources around it, and judging whether the answer holds before it goes to a decision maker.
AI-Augmented Analysis in Sparx EAGovernance: define the standard, let the repository enforce it
Turning the way your practice already models into an MDG Technology and a validation ruleset, reading audit findings as the start of a review conversation, and checking their own work at the source.
AI-Augmented Governance in Sparx EAStakeholder Engagement: make the model an enterprise data source
Deciding what each audience needs to see, getting the data complete, correct and current, and surfacing the insight in the tool the stakeholder already has open, so the architect's time goes to the conversation.
AI-Augmented Stakeholder Engagement in Sparx EA
In the context of their own work
A course on a demo model teaches the buttons. It does not change how Tuesday's work gets done. We teach on your repository, with the AI your organization already licenses, on the backlog your architects have in front of them, so the first deliverables of the new kind are finished during enablement rather than promised after it.
We also work inside the disciplines your architects practice: application portfolio, process, data, systems engineering, solution, software, IT and enterprise architecture, and requirements. The skill is the same; the work it is applied to is theirs.
What it looks like in each disciplineTrain, apply, sustain
A configured solution is only potential. Enablement turns it into everyday practice, and that takes three steps, not one.
Train
A Foundations course on the AI option your practice chose, run on your own repository. Architects leave with a working skill and their first results, not a slide deck.
Apply
We work alongside your architects in their own disciplines on real items from the backlog. The new way of working proves itself on work that matters to them.
Sustain
1:1 mentoring and Sparx Office Hours keep the habits in place as the tools change, and give the whole team consistent behavior and consistent results.
Four ways we enable your team
Foundations courses
Hands-on training in AI-Augmented Architecture on the AI option your practice chose, taught on your own repository. One course per option, so your architects learn in the tool they will use every day.
The five Foundations coursesDiscipline programs
We find the activities in each architect's discipline where AI-augmented work has the most impact, on productivity or on outcomes, and work with the team to do that work the new way on their own backlog.
The disciplines we work in1:1 mentoring
A practitioner alongside an architect or an architecture lead, working through their real problems: how to frame the prompt, what to accept, what to send back, when to stop and talk to the stakeholder. A mentor stands where your own light does not yet reach.
Ask about mentoringSparx Office Hours
A standing session where the whole team brings what they are stuck on, sees how others solved it and keeps up with the tools as they change. It is how early wins turn into the way the practice works.
Ask about Office Hours